In the context of water distribution networks accurate data is critical in predicting future demand, planning emergency scenario simulations, optimization and system modeling. Given the complexity of water distribution networks, any changes or malfunctions within the system will inevitably affect fluid distribution and the supply within.
In order to properly predict and monitor such variables, it may be necessary to create mathematical prediction models of the network incorporating such changes. Available models' accuracy may be greatly affected by unknown system parameters such as change in pipe roughness affecting flow, addition of equipment, or presence of anomalies like leakage.
To this end, once the model is created, it is necessary to utilize known data to ensure such predictions are reliable. A limitation of such modeling procedures is that they approximate the unknown parameters using a short-term sample of hydraulic data, or are limited by insufficient amount of data from a few measuring points which may underrepresent a large size system. Such modeling may create results representing the system hydraulics during the short period of the measuring time but are not expected to accurately represent the system conditions depicting full range of operational conditions and anomalies within. Consequently, any such model may suffer from an inadequate calibration, throwing into question the results after a significant change in the system. Even a series of minor modifications to the system, the model will incrementally depart from that of the original modeling prediction. Thus, even if the calibration is representative of the medium or long-term performance of the system, natural or deliberate changes to the system parameters will introduce further errors into the model.
Accordingly, as time passes the value and effectiveness of the model diminishes. To highlight the effect of time on the accuracy of the model, it will be appreciated that such changes need not be restricted to infrastructure change, but also to deterioration of the network over time and even changes in demand following re-zoning, industrial development and demographic change. The investment of resources, both in terms of time and money, required to generate new models is substantial and inefficient.
A technique for modeling and simulating water distribution and collection systems that includes estimation of future and unknown parameters is greatly desired. Such estimation technique must be more robust and flexible than the existing techniques, to permit model estimation and simulation under more challenging scenarios that prior techniques have not been able to adequately address.